Evaluation of the impact of fetal fibronectin test implementation on hospital admissions for preterm labour in<scp>O</scp>ntario: a multiple baseline time‐series design
Bibliographic record
Abstract
OBJECTIVE: To determine the impact of a health system-wide fetal fibronectin (fFN) testing programme on the rates of hospital admission for preterm labour (PTL). DESIGN: Multiple baseline time-series design. SETTING: Canadian province of Ontario. POPULATION: A retrospective population-based cohort of antepartum and delivered obstetrical admissions in all Ontario hospitals between 1 April 2002 and 31 March 2010. METHODS: International Classification of Diseases codes in a health system-wide hospital administrative database were used to identify the study population and define the outcome measure. An aggregate time series of monthly rates of hospital admissions for PTL was analysed using segmented regression models after aligning the fFN test implementation date for each institution. MAIN OUTCOME MEASURE: Rate of obstetrical hospital admission for PTL. RESULTS: Estimated rates of hospital admission for PTL following fFN implementation were lower than predicted had pre-implementation trends prevailed. The reduction in the rate was modest, but statistically significant, when estimated at 12 months following fFN implementation (-0.96 hospital admissions for PTL per 100 preterm births; 95% confidence interval [CI], -1.02 to -0.90, P = 0.04). The statistically significant reduction was sustained at 24 and 36 months following implementation. CONCLUSIONS: Using a robust quasi-experimental study design to overcome confounding as a result of underlying secular trends or concurrent interventions, we found evidence of a small but statistically significant reduction in the health system-level rate of hospital admissions for PTL following implementation of fFN testing in a large Canadian province.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.020 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".